UDA approaches have explored two main strategies to reduce the domain gap. A large number of works rely on generative adversarial networks (GANs) [1]}, [2]}.
The core idea is to train a GAN such that data points of both domains can be mapped into a domain-agnostic embedding space [3]}.
To this end, a cross-domain discriminator network is trained to classify whether an input data point belongs to the source domain or the target domain. The discriminator network receives its input from a feature generator network. The discriminator network is fooled by the feature generator network which is trained as an adversary to generate domain-agnostic features at its output. As a result, a classifier network that is trained using the source domain annotated data at the output of the generator network would generalize well in the target domain [4]}, [5]}. An area of weakness for GANs is mode collapse which happens and need for fine-tuning the model hyper-parameters.
